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Bayesian Inference

Bayesian Inference is a methodology that employs Bayes Rule to estimate parameters (and their full posterior).

Papers

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Showing 751–775 of 2226 papers

TitleStatusHype
Bidirectional Recurrent Neural Networks as Generative Models - Reconstructing Gaps in Time Series—0
A Tutorial on Sparse Gaussian Processes and Variational Inference—0
Deep importance sampling using tensor trains with application to a priori and a posteriori rare event estimation—0
Alternating linear scheme in a Bayesian framework for low-rank tensor approximation—0
Deep Knowledge Tracing with Learning Curves—0
Deep Learning Aided Laplace Based Bayesian Inference for Epidemiological Systems—0
Deep Learning and Bayesian inference for Inverse Problems—0
Bayesian Eye Tracking—0
BIBI: Bayesian Inference of Breed Composition—0
Deep learning-based prediction of kinetic parameters from myocardial perfusion MRI—0
Deep Learning Surrogates for Real-Time Gas Emission Inversion—0
Deep Maxout Network Gaussian Process—0
Deep Network Regularization via Bayesian Inference of Synaptic Connectivity—0
Bayesian Federated Model Compression for Communication and Computation Efficiency—0
Bias or Optimality? Disentangling Bayesian Inference and Learning Biases in Human Decision-Making—0
Deep reinforcement learning driven inspection and maintenance planning under incomplete information and constraints—0
Attention-Driven Hierarchical Reinforcement Learning with Particle Filtering for Source Localization in Dynamic Fields—0
A compartmental model for Xylella fastidiosa diseases with explicit vector seasonal dynamics—0
Bayesian Flow Networks in Continual Learning—0
Adaptive quadrature schemes for Bayesian inference via active learning—0
Distribution Transformers: Fast Approximate Bayesian Inference With On-The-Fly Prior Adaptation—0
Demystifying excessively volatile human learning: A Bayesian persistent prior and a neural approximation—0
Density Estimation via Bayesian Inference Engines—0
Bayesian Graph Convolutional Neural Networks Using Non-Parametric Graph Learning—0
Biases and Variability from Costly Bayesian Inference—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1F-SWAAccuracy83.61—Unverified
2F-SWAGAccuracy80.93—Unverified